By David A. Kendrick

The skill to conceptualize an financial challenge verbally, to formulate it as a mathematical version, after which symbolize the math in software program in order that the version will be solved on a working laptop or computer is a vital ability for economists. Computational Economics comprises recognized models--and a few brand-new ones--designed to aid scholars stream from verbal to mathematical to computational representations in monetary modeling. The authors' concentration, notwithstanding, isn't just on fixing the types, but additionally on constructing the power to switch them to mirror one's curiosity and perspective. the result's a booklet that permits scholars to be inventive in constructing types which are proper to the industrial difficulties in their times.

not like different computational economics textbooks, this ebook is equipped round financial issues, between them macroeconomics, microeconomics, and finance. The authors hire numerous software program systems--including MATLAB, Mathematica, GAMS, the nonlinear programming solver in Excel, and the database platforms in Access--to let scholars to exploit the main useful process. The e-book progresses from really basic types to extra advanced ones, and contains appendices at the bits and bobs of working each one program.

The ebook is meant to be used by means of complicated undergraduates economists or even, as a primary publicity to computational economics, through graduate students.

- geared up by way of fiscal themes
- Progresses from uncomplicated to extra complicated versions
- contains directions on various software program platforms
- Encourages customization and creativity

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**Extra resources for Computational economics**

**Sample text**

9 It is common in Lagrangian functions to put the objective term first followed by the lambda and the constraint. However, given the sequence of commands we used, Mathematica does things in reverse order. This causes no problem except making the output below slightly harder to comprehend at first. 56 Chapter 3 Partial Equilibrium in Mathematica Once we form the Lagrangian, we compute the first order conditions of the problem as follows In[]:= foc1 = D[eqL, x1] foc2 = D[eqL, x2] foc3 = D[eqL, λ] The Mathematica function D computes the partial derivatives of a function.

6 Demand Curve for x2 3. The Theory of the Firm The standard theory of firm’s behavior assumes that the main goal of the firm is to maximize profits given technology and the prices of output and inputs. To develop a simple example, let’s assume that the firm produces a single output x1 with price p1 , using labor L as a single input and whose price is the wage w. Let’s assume also that the production function is of the form T Lb where T and b are parameters and let’s denote profits by π. nb file as In[]:= pi = p1 T L^b - w L; Notice that we wrote pi instead of π since the Greek letter π is a reserved symbol in Mathematica.

Now back to the Data Set section of the spreadsheet. Check the column at2 and you will find that it is similar to the column at1 except that it uses data from the input data for x4 and x5 to compute the second of the two hidden layer values. Consider next the Output Layer column. It is computed using an expression of the form Output = theta0 + theta1 * at1 + theta2 * at2 where the thetas are weights which are computed in the optimization and that are shown in the section on Output weights near the top of the spreadsheet.